#include <climits>
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"


namespace caffe {

template <typename Dtype>
void Blob<Dtype>::Reshape(const int num, const int channels, const int height, const int width) {
  vector<int> shape(4);
  shape[0] = num;
  shape[1] = channels;
  shape[2] = height;
  shape[3] = width;
  Reshape(shape);
}


template <typename Dtype>
void Blob<Dtype>::Reshape(const vector<int>& shape) {
  CHECK_LE(shape.size(), kMaxBlobAxes);
  count_ = 1;
  shape_.resize(shape.size());
  if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
    shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
  }
  
  int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
  for (int i = 0; i < shape.size(); ++i) {
    CHECK_GE(shape[i], 0);
    if (count_ != 0) {
      CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
    }
    count_ *= shape[i];
    shape_[i] = shape[i];
    shape_data[i] = shape[i];
  }
  
  if (count_ > capacity_) {
    capacity_ = count_;
    data_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
    diff_.reset(new SyncedMemory(capacity_ * sizeof(Dtype)));
  }
}


template <typename Dtype>
void Blob<Dtype>::Reshape(const BlobShape& shape) {
  CHECK_LE(shape.dim_size(), kMaxBlobAxes);
  vector<int> shape_vec(shape.dim_size());
  for (int i = 0; i < shape.dim_size(); ++i) {
    shape_vec[i] = shape.dim(i);
  }
  Reshape(shape_vec);
}


template <typename Dtype>
void Blob<Dtype>::ReshapeLike(const Blob<Dtype>& other) {
  Reshape(other.shape());
}


// capacity_ must be initialized before calling Reshape
template <typename Dtype>
Blob<Dtype>::Blob(const int num, const int channels, const int height, const int width) : capacity_(0) { 
  Reshape(num, channels, height, width); 
}


// capacity_ must be initialized before calling Reshape
template <typename Dtype>
Blob<Dtype>::Blob(const vector<int>& shape) : capacity_(0) { Reshape(shape); }


template <typename Dtype>
const Dtype* Blob<Dtype>::cpu_data() const {
  CHECK(data_);
  return (const Dtype*)data_->cpu_data();
}


template <typename Dtype>
void Blob<Dtype>::set_cpu_data(Dtype* data) {
  CHECK(data);
  data_->set_cpu_data(data);
}


template <typename Dtype>
const Dtype* Blob<Dtype>::cpu_diff() const {
  CHECK(diff_);
  return (const Dtype*)diff_->cpu_data();
}


template <typename Dtype>
Dtype* Blob<Dtype>::mutable_cpu_data() {
  CHECK(data_);
  return static_cast<Dtype*>(data_->mutable_cpu_data());
}


template <typename Dtype>
Dtype* Blob<Dtype>::mutable_cpu_diff() {
  CHECK(diff_);
  return static_cast<Dtype*>(diff_->mutable_cpu_data());
}


template <typename Dtype>
void Blob<Dtype>::ShareData(const Blob& other) {
  CHECK_EQ(count_, other.count());
  data_ = other.data();
}


template <typename Dtype>
void Blob<Dtype>::ShareDiff(const Blob& other) {
  CHECK_EQ(count_, other.count());
  diff_ = other.diff();
}


// The "update" method is used for parameter blobs in a Net, which are stored as Blob<float> or Blob<double>
// -- hence we do not define it for Blob<int> or Blob<unsigned int>.
template <> void Blob<unsigned int>::Update() { NOT_IMPLEMENTED; }
template <> void Blob<int>::Update() { NOT_IMPLEMENTED; }
template <> void Blob<bool>::Update() { NOT_IMPLEMENTED; }

template <typename Dtype>
void Blob<Dtype>::Update() {
  // We will perform update based on where the data is located.
  switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      // perform computation on CPU
      caffe_axpy<Dtype>(count_, Dtype(-1),
          static_cast<const Dtype*>(diff_->cpu_data()),
          static_cast<Dtype*>(data_->mutable_cpu_data()));
      break;
    default:
      LOG(FATAL) << "Syncedmem not initialized.";
  }
}


template <> unsigned int Blob<unsigned int>::asum_data() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <> int Blob<int>::asum_data() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <> bool Blob<bool>::asum_data() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <typename Dtype>
Dtype Blob<Dtype>::asum_data() const {
  if (!data_) { return 0; }
  switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      return caffe_cpu_asum(count_, cpu_data());
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
  return 0;
}


template <> unsigned int Blob<unsigned int>::asum_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <> int Blob<int>::asum_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <> bool Blob<bool>::asum_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <typename Dtype>
Dtype Blob<Dtype>::asum_diff() const {
  if (!diff_) { return 0; }
  switch (diff_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      return caffe_cpu_asum(count_, cpu_diff());
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
  }
  return 0;
}


template <> unsigned int Blob<unsigned int>::sumsq_data() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <> int Blob<int>::sumsq_data() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <> bool Blob<bool>::sumsq_data() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <typename Dtype>
Dtype Blob<Dtype>::sumsq_data() const {
  Dtype sumsq;
  const Dtype* data;
  if (!data_) { return 0; }
  switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      data = cpu_data();
      sumsq = caffe_cpu_dot(count_, data, data);
      break;
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
  return sumsq;
}


template <> unsigned int Blob<unsigned int>::sumsq_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}


template <> int Blob<int>::sumsq_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <> bool Blob<bool>::sumsq_diff() const {
  NOT_IMPLEMENTED;
  return 0;
}

template <typename Dtype>
Dtype Blob<Dtype>::sumsq_diff() const {
  Dtype sumsq;
  const Dtype* diff;
  if (!diff_) { return 0; }
  
  switch (diff_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      diff = cpu_diff();
      sumsq = caffe_cpu_dot(count_, diff, diff);
      break;
    case SyncedMemory::UNINITIALIZED:
      return 0;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
  return sumsq;
}


template <> void Blob<unsigned int>::scale_data(unsigned int scale_factor) {
  NOT_IMPLEMENTED;
}

template <> void Blob<int>::scale_data(int scale_factor) {
  NOT_IMPLEMENTED;
}

template <> void Blob<bool>::scale_data(bool scale_factor) {
  NOT_IMPLEMENTED;
}

template <typename Dtype>
void Blob<Dtype>::scale_data(Dtype scale_factor) {
  Dtype* data;
  if (!data_) { return; }
  
  switch (data_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      data = mutable_cpu_data();
      caffe_scal(count_, scale_factor, data);
      return;
    case SyncedMemory::UNINITIALIZED:
      return;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
  }
}


template <> void Blob<unsigned int>::scale_diff(unsigned int scale_factor) {
  NOT_IMPLEMENTED;
}


template <> void Blob<int>::scale_diff(int scale_factor) {
  NOT_IMPLEMENTED;
}


template <> void Blob<bool>::scale_diff(bool scale_factor) {
  NOT_IMPLEMENTED;
}


template <typename Dtype>
void Blob<Dtype>::scale_diff(Dtype scale_factor) {
  Dtype* diff;
  if (!diff_) { return; }
  
  switch (diff_->head()) {
    case SyncedMemory::HEAD_AT_CPU:
      diff = mutable_cpu_diff();
      caffe_scal(count_, scale_factor, diff);
      return;
    case SyncedMemory::UNINITIALIZED:
      return;
    default:
      LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
  }
}


template <typename Dtype>
bool Blob<Dtype>::ShapeEquals(const BlobProto& other) {
  if (other.has_num() || other.has_channels() || other.has_height() || other.has_width()) {
    // Using deprecated 4D Blob dimensions -- shape is (num, channels, height, width).
    // Note: we do not use the normal Blob::num(), Blob::channels(), etc.
    // methods as these index from the beginning of the blob shape, where legacy
    // parameter blobs were indexed from the end of the blob shape (e.g., bias
    // Blob shape (1 x 1 x 1 x N), IP layer weight Blob shape (1 x 1 x M x N)).
    return shape_.size() <= 4 && LegacyShape(-4) == other.num() && LegacyShape(-3) == other.channels() &&
           LegacyShape(-2) == other.height() && LegacyShape(-1) == other.width();
  }
  
  vector<int> other_shape(other.shape().dim_size());
  for (int i = 0; i < other.shape().dim_size(); ++i) {
    other_shape[i] = other.shape().dim(i);
  }
  return shape_ == other_shape;
}


template <typename Dtype>
void Blob<Dtype>::CopyFrom(const Blob& source, bool copy_diff, bool reshape) {
  if (source.count() != count_ || source.shape() != shape_) {
    if (reshape) {
      ReshapeLike(source);
    } else {
      LOG(FATAL) << "Trying to copy blobs of different sizes.";
    }
  }

  switch (Caffe::mode()) {
  case Caffe::CPU:
    if (copy_diff) {
      caffe_copy(count_, source.cpu_diff(), static_cast<Dtype*>(diff_->mutable_cpu_data()));
    } else {
      caffe_copy(count_, source.cpu_data(), static_cast<Dtype*>(data_->mutable_cpu_data()));
    }
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
}


template <typename Dtype>
void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
  if (reshape) {
    vector<int> shape;
    if (proto.has_num() || proto.has_channels() || proto.has_height() || proto.has_width()) {
      // Using deprecated 4D Blob dimensions -- shape is (num, channels, height, width).
      shape.resize(4);
      shape[0] = proto.num();
      shape[1] = proto.channels();
      shape[2] = proto.height();
      shape[3] = proto.width();
    } else {
      shape.resize(proto.shape().dim_size());
      for (int i = 0; i < proto.shape().dim_size(); ++i) {
        shape[i] = proto.shape().dim(i);
      }
    }
    Reshape(shape);
  } else {
    CHECK(ShapeEquals(proto)) << "shape mismatch (reshape not set)";
  }
  
  // copy data
  Dtype* data_vec = mutable_cpu_data();
  if (proto.double_data_size() > 0) {
    CHECK_EQ(count_, proto.double_data_size());
    for (int i = 0; i < count_; ++i) {
      data_vec[i] = proto.double_data(i);
    }
  } else {
    CHECK_EQ(count_, proto.data_size());
    for (int i = 0; i < count_; ++i) {
      data_vec[i] = proto.data(i);
    }
  }
  
  if (proto.double_diff_size() > 0) {
    CHECK_EQ(count_, proto.double_diff_size());
    Dtype* diff_vec = mutable_cpu_diff();
    for (int i = 0; i < count_; ++i) {
      diff_vec[i] = proto.double_diff(i);
    }
  } else if (proto.diff_size() > 0) {
    CHECK_EQ(count_, proto.diff_size());
    Dtype* diff_vec = mutable_cpu_diff();
    for (int i = 0; i < count_; ++i) {
      diff_vec[i] = proto.diff(i);
    }
  }
}


template <>
void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
  proto->clear_shape();
  for (int i = 0; i < shape_.size(); ++i) {
    proto->mutable_shape()->add_dim(shape_[i]);
  }
  
  proto->clear_double_data();
  proto->clear_double_diff();
  const double* data_vec = cpu_data();
  
  for (int i = 0; i < count_; ++i) {
    proto->add_double_data(data_vec[i]);
  }
  
  if (write_diff) {
    const double* diff_vec = cpu_diff();
    for (int i = 0; i < count_; ++i) {
      proto->add_double_diff(diff_vec[i]);
    }
  }
}


template <>
void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
  proto->clear_shape();
  for (int i = 0; i < shape_.size(); ++i) {
    proto->mutable_shape()->add_dim(shape_[i]);
  }
  
  proto->clear_data();
  proto->clear_diff();
  const float* data_vec = cpu_data();
  
  for (int i = 0; i < count_; ++i) {
    proto->add_data(data_vec[i]);
  }
  
  if (write_diff) {
    const float* diff_vec = cpu_diff();
    for (int i = 0; i < count_; ++i) {
      proto->add_diff(diff_vec[i]);
    }
  }
}


INSTANTIATE_CLASS(Blob);
template class Blob<bool>;
template class Blob<int>;
template class Blob<unsigned int>;

}  // namespace caffe
